UBER mode in 2.X :
Normally mappers and reducers will run by ResourceManager (RM), RM will create separate container for mapper and reducer. Uber configuration, will allow to run mapper and reducers in the same process as the ApplicationMaster (AM).
Uber jobs :
Uber jobs are jobs that are executed within the MapReduce ApplicationMaster rather than communicate with RM to create the mapper and reducer containers. The AM runs the map and reduce tasks within its own process and avoided the overhead of launching and communicate with remote containers.
Why is it so :
If you have a small dataset or you want to run MapReduce on small amount of data, Uber configuration will help you out, by reducing additional time that MapReduce normally spends mapper and reducers phase.
Can I configure/have a Uber for all MapReduce job.?
As of now, map-only jobs and jobs with one reducer are supported.
In other word :
Uber Job occurs when multiple mapper and reducers are combined to use a single container. There are four core settings around the configuration of Uber Jobs in the mapred-site.xml. Configuration options for Uber Jobs:
mapreduce.job.ubertask.enable
mapreduce.job.ubertask.maxmaps
mapreduce.job.ubertask.maxreduces
mapreduce.job.ubertask.maxbytes
Normally mappers and reducers will run by ResourceManager (RM), RM will create separate container for mapper and reducer. Uber configuration, will allow to run mapper and reducers in the same process as the ApplicationMaster (AM).
Uber jobs :
Uber jobs are jobs that are executed within the MapReduce ApplicationMaster rather than communicate with RM to create the mapper and reducer containers. The AM runs the map and reduce tasks within its own process and avoided the overhead of launching and communicate with remote containers.
Why is it so :
If you have a small dataset or you want to run MapReduce on small amount of data, Uber configuration will help you out, by reducing additional time that MapReduce normally spends mapper and reducers phase.
Can I configure/have a Uber for all MapReduce job.?
As of now, map-only jobs and jobs with one reducer are supported.
In other word :
Uber Job occurs when multiple mapper and reducers are combined to use a single container. There are four core settings around the configuration of Uber Jobs in the mapred-site.xml. Configuration options for Uber Jobs:
mapreduce.job.ubertask.enable
mapreduce.job.ubertask.maxmaps
mapreduce.job.ubertask.maxreduces
mapreduce.job.ubertask.maxbytes
When ApplicationMaster can calculate that the overhead of negotiating resources with ResourceManager, communicating with NodeManagers on different nodes to launch the containers and running the tasks on those containers is much more than running MapReduce job sequentially, it can run a job as uber task.
ReplyDeletehttps://knpcode.com/hadoop/yarn/uber-task-yarn/